Results of AugMix combined with \(\operatorname{\mathbf {\ell }}_2\)  and \(\operatorname{\mathbf {\ell }}_\infty \)  adversarial training.
We report these results on CIFAR-10 in Table REF . We can observe that RLAT still outperforms \(\operatorname{\mathbf {\ell }}_2\)  and \(\operatorname{\mathbf {\ell }}_\infty \)  adversarial training when they are combined with AugMix, both with and without the JSD consistency term from [1]}.
Moreover, our RLAT model achieves 89.4% accuracy with the JSD term outperforming the best model from the AugMix paper [1]} despite being much smaller: we use ResNet-18 while the best model of [1]} uses ResNeXt-29.
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